Evidence-Based Participatory Research Approaches for Last Mile Adolescent Girls and Young Women: A Reflection on the Study Methods
Bibliographic record
Abstract
There is increased interest in methods to achieve meaningful inclusion of traditionally marginalized and last mile populations in research. Adolescent girls and young women (AGYW) working in the artisanal small-scale mining (ASM) are an extremely marginalized group who, in addition to being excluded from decision making processes, tend to be excluded from research- even when the research is about them. There is a paucity of literature on the methods that can be used to meaningfully engage AGYW in ASM with limited education and knowledge in research. This paper provides a detailed description and reflection on a participatory, mixed-methods research process (involving a document review, survey, key informant interviews, focus group discussions and the nominal group technique) through which AGYW in ASM communities in Uganda and Ghana were involved to identify priority interventions to support their economic and health resilience. While the process, due to various challenges, took over 10 months to complete, it strengthened the AGYW’s capacity to meaningfully participate in the project and in decision making. Broad stakeholder engagement resulted in identifying relevant interventions whose implementation will improve the health and well-being of this last mile population. This paper highlights the contributions and challenges of using participatory research approaches among AGYW and other traditionally marginalized under researched populations to ensure that their voices are prioritized. Such participatory approaches are useful in ensuring that such last mile populations are not left behind on the path to achieving the 2030 Sustainable Development Goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.266 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".